With the advancement and proliferation of computer technology, online shopping, also known as e-commerce, has become one of the major avenues of commerce. Consumers and businesses are purchasing goods from online vendors more frequently than ever, and the number of transactions and sales revenue are projected to grow year-over-year at a staggering rate. As the scope and volume of e-commerce continue to grow, both the number of different items available online and the average number of purchases made in a given period are also growing exponentially. For example, the number of different items sold by one popular online retailer is said to have reached more than 600 million products, and the number of packages shipped per day by the same retailer, 1.6 million.
Each online purchase, by nature, requires a delivery of the purchased goods to its intended recipient. Each online purchase or order typically comprises of one or more goods, wherein the one or more goods can be packaged into one or more packages, each with its own promised delivery date. A typical order may be processed via steps such as: receiving, from a customer, an order for one or more goods; retrieving the one or more goods from an inventory; packaging the one or more goods into one or more packages; and delivering the one or more packages to the intended recipient before the promised delivery date. The promised delivery date may be set by the retailer itself or a shipping courier, or a specific date may be requested by the customer, which then may be assigned as the promised delivery date. An ideal system of order processing would deliver each package to the intended recipient by the promised delivery date without failure.
Currently existing order processing systems include a varying degree of automation and complexity in implementing the steps described above. With increasing number of different goods and orders, however, aggravated by the fact that the orders need to go through a complex network of subsystems and that some orders have complicating factors such as a partial return, current systems are problematic in that they are incapable of or largely inefficient at tracking individual packages from the moment an order is placed to the moment the order is fulfilled (i.e., every package in the order is delivered to the intended recipient or returned to the inventory). This problem is aggravated by the fact that increasing number of packages and focus on expeditious processing makes the system more prone to human error, such as omitting a package, mislabeling, or mis-sorting. For example, an order comprising of multiple packages with different promised delivery dates may end up with one or more lost or damaged packages mid-way through the system, which the system may not notice until a frustrated customer follows up.
In another example, one of the packages of the order may be delayed at some point in the system and a customer may request a redelivery of the package, in which case the system will need to reorder a new package because the system cannot tell why the existing package is delayed or how long it will need in order to clear the delay. In this case, both the existing delayed package and the new package may get delivered to the customer, incurring an unnecessary expense to the system. Even in some cases where the existing delayed package is correctly routed back to a warehouse, current systems may not be able to distinguish it from a package returned by a customer, requiring the delayed package to go through a full inspection process along with other customer-returned packages when it could have been set aside and restocked with only a minimal inspection as it had not reached a customer and thus was not opened. These scenarios serve to exemplify shortcomings of current systems and many other problems may also be apparent to those of ordinary skill in the art.
Therefore, there is a need for improved methods and systems for tracking orders and packages through the order processing system and proactively identifying and taking necessary actions to reduce the number of lingering orders that have not been delivered yet, all the while minimizing its impact on operating expenses.